IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v13y2025i3p526-d1584235.html
   My bibliography  Save this article

A Group Consensus Measure That Takes into Account the Relative Importance of the Decision-Makers

Author

Listed:
  • József Dombi

    (HUN-REN SZTE, Research Group on Artificial Intelligence, 6701 Szeged, Hungary
    Institute of Informatics, University of Szeged, 6701 Szeged, Hungary
    These authors contributed equally to this work.)

  • Jenő Fáró

    (Faculty of Economics, Eötvös Loránd University, 1053 Budapest, Hungary
    These authors contributed equally to this work.)

  • Tamás Jónás

    (Faculty of Economics, Eötvös Loránd University, 1053 Budapest, Hungary
    These authors contributed equally to this work.)

Abstract

In group decision making, the knowledge, skills, and experience of the decision-makers may not be at the same level. Hence, the need arises to take into account not only the opinion, but also the relative importance of the opinion of each decision-maker. These relative importance values can be treated as weights. In a group decision making situation, it is not only the weighted aggregate output that matters, but also the weighted measure of the group consensus. Noting that weighted group consensus measures have not yet been intensely studied, in this study, based on well-known requirements for non-weighted consensus measures, we define six reasonable requirements for the weighted case. Then, we propose a function family and prove that it satisfies the above requirements for a weighted consensus measure. Hence, the proposed measure can be used in group decision making situations where the decision-makers have various weight values that reflect the relative importance of their opinions. The proposed weighted consensus measure is based on the fuzziness degree of the decumulative distribution function of the input scores, taking into account the weights. Hence, it may be viewed as a weighted adaptation of the so-called fuzziness measure-based consensus measure. The novel weighted consensus measure is determined by a fuzzy entropy function; i.e., this function may be regarded as a generator of the consensus measure. This property of the proposed weighted consensus measure family makes it very versatile and flexible. The nice properties of the proposed weighted consensus measure family are demonstrated by means of concrete numerical examples.

Suggested Citation

  • József Dombi & Jenő Fáró & Tamás Jónás, 2025. "A Group Consensus Measure That Takes into Account the Relative Importance of the Decision-Makers," Mathematics, MDPI, vol. 13(3), pages 1-24, February.
  • Handle: RePEc:gam:jmathe:v:13:y:2025:i:3:p:526-:d:1584235
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/13/3/526/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/13/3/526/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. González-Arteaga, T. & Alcantud, J.C.R. & de Andrés Calle, R., 2016. "A cardinal dissensus measure based on the Mahalanobis distance," European Journal of Operational Research, Elsevier, vol. 251(2), pages 575-585.
    2. Porcu, Emilio & Mateu, Jorge & Christakos, George, 2009. "Quasi-arithmetic means of covariance functions with potential applications to space-time data," Journal of Multivariate Analysis, Elsevier, vol. 100(8), pages 1830-1844, September.
    3. József Dombi & Jenő Fáró & Tamás Jónás, 2023. "A Fuzzy Entropy-Based Group Consensus Measure for Financial Investments," Mathematics, MDPI, vol. 12(1), pages 1-18, December.
    4. Dombi, József & Jónás, Tamás, 2022. "Weighted aggregation systems and an expectation level-based weighting and scoring procedure," European Journal of Operational Research, Elsevier, vol. 299(2), pages 580-588.
    5. Yan, Hong-Bin & Ma, Tieju & Huynh, Van-Nam, 2017. "On qualitative multi-attribute group decision making and its consensus measure: A probability based perspective," Omega, Elsevier, vol. 70(C), pages 94-117.
    6. J. C. R. Alcantud & R. Andrés Calle & J. M. Cascón, 2015. "Pairwise Dichotomous Cohesiveness Measures," Group Decision and Negotiation, Springer, vol. 24(5), pages 833-854, September.
    7. Jorge Alcalde-Unzu & Marc Vorsatz, 2013. "Measuring the cohesiveness of preferences: an axiomatic analysis," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 41(4), pages 965-988, October.
    8. Dombi, József & Jónás, Tamás, 2024. "Consensus measures based on a fuzzy concept," European Journal of Operational Research, Elsevier, vol. 315(2), pages 642-653.
    9. Chiclana, F. & Herrera-Viedma, E. & Herrera, F. & Alonso, S., 2007. "Some induced ordered weighted averaging operators and their use for solving group decision-making problems based on fuzzy preference relations," European Journal of Operational Research, Elsevier, vol. 182(1), pages 383-399, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. González-Arteaga, T. & Alcantud, J.C.R. & de Andrés Calle, R., 2016. "A cardinal dissensus measure based on the Mahalanobis distance," European Journal of Operational Research, Elsevier, vol. 251(2), pages 575-585.
    2. Alexander Karpov, 2017. "Preference Diversity Orderings," Group Decision and Negotiation, Springer, vol. 26(4), pages 753-774, July.
    3. José Carlos R. Alcantud & María José M. Torrecillas, 2017. "Consensus measures for various informational bases. Three new proposals and two case studies from political science," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(1), pages 285-306, January.
    4. Rodríguez Alcantud, José Carlos & de Andrés Calle, Rocío & González-Arteaga, Teresa, 2013. "Codifications of complete preorders that are compatible with Mahalanobis disconsensus measures," MPRA Paper 50533, University Library of Munich, Germany.
    5. Matthew Gentzkow & Jesse M. Shapiro & Matt Taddy, 2019. "Measuring Group Differences in High‐Dimensional Choices: Method and Application to Congressional Speech," Econometrica, Econometric Society, vol. 87(4), pages 1307-1340, July.
    6. Peide Liu & Hongyu Yang & Haiquan Wu & Meilong Ju & Fawaz E. Alsaadi, 2019. "Some Maclaurin Symmetric Mean Aggregation Operators Based on Cloud Model and Their Application to Decision-Making," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(03), pages 981-1007, May.
    7. Junling Zhang & Xiaowen Qi & Changyong Liang, 2018. "Tackling Complexity in Green Contractor Selection for Mega Infrastructure Projects: A Hesitant Fuzzy Linguistic MADM Approach with considering Group Attitudinal Character and Attributes’ Interdependen," Complexity, Hindawi, vol. 2018, pages 1-31, December.
    8. Russ McBride & Mark D. Packard & Brent B. Clark, 2024. "Rogue Entrepreneurship," Entrepreneurship Theory and Practice, , vol. 48(1), pages 392-417, January.
    9. Zhou-Jing Wang & Yuhong Wang & Kevin W. Li, 2016. "An Acceptable Consistency-Based Framework for Group Decision Making with Intuitionistic Preference Relations," Group Decision and Negotiation, Springer, vol. 25(1), pages 181-202, January.
    10. Cheng, Yujie & Song, Dengwei & Wang, Zhenya & Lu, Chen & Zerhouni, Noureddine, 2020. "An ensemble prognostic method for lithium-ion battery capacity estimation based on time-varying weight allocation," Applied Energy, Elsevier, vol. 266(C).
    11. Pingtao Yi & Qiankun Dong & Weiwei Li, 2021. "A family of IOWA operators with reliability measurement under interval-valued group decision-making environment," Group Decision and Negotiation, Springer, vol. 30(3), pages 483-505, June.
    12. Peeters, R.J.A.P. & Wolk, K.L., 2015. "Forecasting with Colonel Blotto," Research Memorandum 025, Maastricht University, Graduate School of Business and Economics (GSBE).
    13. Montero, José-María, 2018. "Geostatistics: Unde venis et quo vadis? /Geoestadística:¿De dónde vienes y a dónde vas?," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 36, pages 81-106, Enero.
    14. Yan, Hong-Bin & Li, Ming, 2022. "Consumer demand based recombinant search for idea generation," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    15. Zhang, Bowen & Dong, Yucheng & Zhang, Hengjie & Pedrycz, Witold, 2020. "Consensus mechanism with maximum-return modifications and minimum-cost feedback: A perspective of game theory," European Journal of Operational Research, Elsevier, vol. 287(2), pages 546-559.
    16. Emilio Porcu & Moreno Bevilacqua & Marc G. Genton, 2016. "Spatio-Temporal Covariance and Cross-Covariance Functions of the Great Circle Distance on a Sphere," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 888-898, April.
    17. József Dombi & Jenő Fáró & Tamás Jónás, 2023. "A Fuzzy Entropy-Based Group Consensus Measure for Financial Investments," Mathematics, MDPI, vol. 12(1), pages 1-18, December.
    18. Fernandez, Eduardo & Olmedo, Rafael, 2013. "An outranking-based general approach to solving group multi-objective optimization problems," European Journal of Operational Research, Elsevier, vol. 225(3), pages 497-506.
    19. Liu Fang & Peng Yanan & Zhang Weiguo & Pedrycz Witold, 2017. "On Consistency in AHP and Fuzzy AHP," Journal of Systems Science and Information, De Gruyter, vol. 5(2), pages 128-147, April.
    20. B. Ahn & S. Choi, 2012. "Aggregation of ordinal data using ordered weighted averaging operator weights," Annals of Operations Research, Springer, vol. 201(1), pages 1-16, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:13:y:2025:i:3:p:526-:d:1584235. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.